Paper Info
Title
An Improved Single Node Genetic Programming for Symbolic Regression.
Abstract
This paper presents a first step of our research on designing an effective and efficient GP-based method for solving the symbolic regression. We have proposed three extensions of the standard Single Node GP, namely (1) a selection strategy for choosing nodes to be mutated based on the depth of the nodes, (2) operators for placing a compact version of the best tree to the beginning and to the end of the population, and (3) a local search strategy with multiple mutations applied in each iteration. All the proposed modifications have been experimentally evaluated on three symbolic regression problems and compared with standard GP and SNGP. The achieved results are promising showing the potential of the proposed modifications to significantly improve the performance of the SNGP algorithm.
Year
DOI
Venue
2015
10.5220/0005598902440251
IJCCI (ECTA)
Keywords
Field
DocType
Genetic Programming,Single Node Genetic Programming,Symbolic Regression
Population,Regression,Computer science,Algorithm,Genetic programming,Operator (computer programming),Local search (optimization),Symbolic regression
Conference
Volume
ISBN
Citations 
1
978-1-5090-1968-7
0
PageRank 
References 
Authors
0.34
5
2
Name
Order
Citations
PageRank
Jirí Kubalík1116.50
Robert Babuska22200164.90